code: def cnn(opt):
      nn = Sequential()
      nn.add(
          Conv2D(
              opt["filters.0"],
              (3, 3),
              padding="same",
              input_shape=X_train.shape[1:],
          )
      )
      nn.add(Activation("relu"))
      nn.add(Conv2D(opt["filters.0"], (3, 3)))
      nn.add(Activation("relu"))
      nn.add(MaxPooling2D(pool_size=(2, 2)))
      nn.add(Dropout(0.25))
  
      nn.add(Conv2D(opt["filters.0"], (3, 3), padding="same"))
      nn.add(Activation("relu"))
      nn = opt["conv_layer.0"](nn)
      nn.add(Dropout(0.25))
  
      nn.add(Flatten())
      nn.add(Dense(opt["neurons.0"]))
      nn.add(Activation("relu"))
      nn.add(Dropout(0.5))
      nn.add(Dense(10))
      nn.add(Activation("softmax"))
  
      nn.compile(
          optimizer="adam",
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
      nn.fit(X_train, y_train, epochs=5, batch_size=256)
  
      _, score = nn.evaluate(x=X_test, y=y_test)
  
      return score
  
file_path: Hyperactive/examples/optimization_applications/neural_architecture_search.py
imports: import numpy
  from keras.models import Sequential
  from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Activation, Dropout
  from keras.datasets import cifar10
  from keras.utils import to_categorical
  from hyperactive import Hyperactive
label_desc: Use image node_id 4 for calling a global function with example usage: cnn(opt) and returns: score